Multiple People Tracking Using Hierarchical Deep Tracklet Re-identification

Abstract

The task of multiple people tracking in monocular videos is challenging because of the numerous difficulties involved: occlusions, varying environments, crowded scenes, camera parameters and motion. In the tracking-by-detection paradigm, most approaches adopt person re-identification techniques based on computing the pairwise similarity between detections. However, these techniques are less effective in handling long-term occlusions. By contrast, tracklet (a sequence of detections) re-identification can improve association accuracy since tracklets offer a richer set of visual appearance and spatio-temporal cues. In this paper, we propose a tracking framework that employs a hierarchical clustering mechanism for merging tracklets. To this end, tracklet re-identification is performed by utilizing a novel multi-stage deep network that can jointly reason about the visual appearance and spatio-temporal properties of a pair of tracklets, thereby providing a robust measure of affinity. Experimental results on the challenging MOT16 and MOT17 benchmarks show that our method significantly outperforms state-of-the-arts.

arXiv comment13 pages (8 main + 2 bibliography + 5 appendices)

References in corpus (13)

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Cited by in corpus (6)

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• Local Metrics for Multi-Object Tracking
• Learning a Proposal Classifier for Multiple Object Tracking
• State-aware Re-identification Feature for Multi-target Multi-camera Tracking
• Enhancing the Association in Multi-Object Tracking via Neighbor Graph